about
Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspectiveGaussian mixture models and semantic gating improve reconstructions from human brain activity.Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks.A review on the computational methods for emotional state estimation from the human EEGUnsupervised feature learning improves prediction of human brain activity in response to natural images.Deep learning in bioinformatics.Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.Accurate Reconstruction of Image Stimuli From Human Functional Magnetic Resonance Imaging Based on the Decoding Model With Capsule Network Architecture
P2860
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P2860
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh-hant
name
Neural decoding with hierarchical generative models.
@en
Neural decoding with hierarchical generative models.
@nl
type
label
Neural decoding with hierarchical generative models.
@en
Neural decoding with hierarchical generative models.
@nl
prefLabel
Neural decoding with hierarchical generative models.
@en
Neural decoding with hierarchical generative models.
@nl
P2860
P356
P1433
P1476
Neural decoding with hierarchical generative models.
@en
P2093
Marcel A J van Gerven
P2860
P304
P356
10.1162/NECO_A_00047
P577
2010-09-21T00:00:00Z